The Polarization Footprint Methodology

October 17, 2025
Publications

The polarization footprint is a cross-platform measure that ranks social media platforms according to the prevalence of affective polarization present on the platform. The polarization footprint relies on observing content and relationships on social media platforms, and is designed in such a way as to be comparable across platforms. The methodology is explicitly designed to deliver defendable and confident measures of the minimum prevalence of polarization. Showing platform users the numeric reflection and representation of polarizing behaviour may spur reflection and potentially policy or behavioural modifications.

As such, the polarization footprint method:

  • provides a template for how on-platform polarization can be measured using ecologically valid observational methods (that is, non-experimental, non-survey methods) by external researchers (i.e. researchers not working within platform companies);
  • explores how league tables (comparing platforms) can incentivize platform reform and inform user choice; and
  • spurs discussion of platform responsibilities with respect to conflict and polarization that informs policy about how the interaction of online platforms with societal conflict should be regulated, including by considering taxation on the polarization footprint.

To complement the polarization footprint, this method suggests running the Neely Social Media Index survey alongside the on-platform observational measure. The objective of the survey is to explore positive and negative user experiences on social media platforms. By running the survey together with the on-platform measure, we can derive some meaning from the relationship between their results, and understand the differences between perceived negative experiences and observable polarization. This method was used by Build Up in Kenya in 2025. Where operational decisions were made specifically for the Kenyan context, this is indicated, in order to facilitate adaptation and replication to other contexts.

Rita

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